Stop trusting black-box AI for SQL – I built a step-based compiler that generates verifiable, hallucination-free queries

Hey IH,

I’ve been watching the “AI + SQL” space, and one thing keeps bugging me: even the best LLMs hallucinate tables, columns, or logic. You can’t just trust the output – you have to painfully review every nested subquery and window function.

So I built SQLazy differently.

The old way (most AI tools):

You ask in English → AI writes the final SQL directly.

It might look correct, but you never know until it fails in production.

Debugging? Good luck tracing through 10 layers of CTEs.

The SQLazy way:

You describe each step in natural language (e.g., “filter stock=100046”, “sort by date”, “mark if price drops”).

The compiler turns those steps into standard SQL – no LLM involved in the final generation.

You can preview and debug every step, see intermediate tables.

The output is deterministic and fully auditable.

What that means:

✅ Zero SQL hallucinations from the compiler.

✅ You stay in control, AI just helps you write steps faster.

✅ Complex analytics (consecutive streaks, sessionization, conditional grouping) become simple building blocks.

✅ Works across MySQL, PG, Oracle – Snowflake/BigQuery coming.

Example from the repo – “Longest rising streak for a stock”:

Raw SQL: 25+ lines of nested window functions – hard to review.

SQLazy workflow: 4 simple steps (filter → sort → segment → summarize). You verify logic in 30 seconds.

Try it:

Web playground (no signup): https://sqlazy.com

Desktop IDE (bring your own LLM key, unlimited local debug): download from GitHub.

Repo with real examples: https://github.com/SPLWare/SQLazy

Not for everyone – if you just need SELECT * FROM table, you don't need this. But if you’ve ever spent an hour debugging a window function or rewriting SQL for a different database, SQLazy might save your week.

Would love feedback – especially from those who’ve tried (and been burned by) AI SQL tools.